Final Exam PreparationPHI 2100-01- Note: Any material from the notes, readings, lectures may be on the exam.Concepts- you should be able to explain and recognize examples of:Representativeness Heuristic-We tend to make judgments by using stereotypes. (Our idea of the representative X.) Clustering Illusion- Our “stereotype” of random sequences is less streaky than real random sequences. Therefore, we’re surprised by how streaky random sequences are.NOTE: This is why gamblers believe in hot & cold streaks when it comes to gambling. We are SURPRISED by how streaky chance sequences are. Base Rate Paradox- a statical result where false positives tests are more probable the true positives test, occurring when the overall population has a low incidence of a condition and the incident rate is lower than the false positive rate. Use Baye's theorem. Conjunction Fallacy- A more specific / demanding description that fits our stereotype will be seen as more probable than a less specific / demanding description that does not fit our stereotype. Easy Explanation (Hindsight Bias)- These “easy” explanations often give you a FALSE sense of understanding. Whenever you offer an explanation for some piece of behavior, you should ask yourself two questions- It gives you a FALSE sense of understanding. - deals with Hindsight Bias—we are naturally story tellers, and so we are very good convincing butunsupported explanations for things after they occur. (1) Consider the opposite: If something else had occurred, could I have explained that too?If YES: Probably an “easy” explanation that offers a false sense of understanding(2) Prediction: Could I have predicted this BEFORE it happened? If NO: Probably an “easy” explanation that offers a false sense of understanding.o Examples: John’s mom is a bartender. John worked at the bar during high school. If aftercollege he goes to work at the bar, people can explain it as follows: he missed the bar. If he doesn’t, people can explain it as follows: it’s the only job he’s ever had (he hates it).Regression Fallacy- We often explain regression effects with unnecessary causal factors. Saying one causes the other when they really have nothing in common.Regression to the Mean: Whenever occurrences of X vary around a mean, if X1 is extreme, X2 is likely to be closer to the mean…Availability Heuristic (convenient memory)- We tend to think events are more probable to the extent they are more available to memory. Often the availability heuristic works well. Often, events are available to memory because they really do happen a lot. It is a mental shortcut that occurs when people make judgments about the probability of events by how easy it is to think of examples.BUT: sometimes, our memory is selective and we remember things that are VIVID or DRAMATIC or ANNOYING of FIT W/OUR THEORIES.- This tends to happen when events are two-sided- Two-sided events: both potential outcomes would be equally noticed or remembered; one-sided events: only one outcome would be noticed, remain in memory. - Some events that are inherently one sided: o Hedonic asymmetries —only one outcome arouses emotion or requires an act on your part. Ex. “all of the buses are heading the wrong direction”o Pattern asymmetries —you tend to remember events that stand out, or seem to be the result of a “pattern.” Ex. Always saying that you woke up in the middle of the night at “1:23” very often. o Definitional asymmetries : one-sided almost by definition. Ex. “I can always tell when someone has had plastic surgery (when you do detect it, you notice and remember, but if you can’t tell that someone has had plastic surgery then you don’t have any information about it, usually). Biased Interpretation-We often interpret data that contradicts our pet beliefs as BAD LUCK or as ALMOST CONFIRMING our views. Fortune Cookie Problem- no specific prediction, the prediction is so vague and general that it could be applied to almost everyone; NO experience would disconfirm your pet belief; NO time frame and vague. - Ask yourself: is there any experience I could have that would convince me my pet belief was false? If not, you may be the victim of the fortune cookie problem. - Ex. “good things come to those who wait.” The time frame is ambiguous—eventually good things happen to most people. Optional Stopping- Negative Evidence ->Critical scrutiny ->Explain away STOP: No more critical scrutinyPositive Evidence -> STOP: No more critical scrutiny- Are you equally critical of reasons that tend to support your view of something that is important to you (i.e. politics, abortion, etc.) as you are of reasons that tend to undermine your view? PROBABLY NOT which Then: you are probably guilty of optional stopping.- Note: you’re not IGNORING negative evidence, you are rather treating negative and positive evidence DIFFERENTLY (so it would be unfair to criticize you for being close-minded, on the other hand, it might be easier to correct our mistakes if we were just ignoring negative evidence. Correction would just involve attending to all the relevant evidence. Sharpening & Leveling- Deals with second-hand testimony; Testimony is sharpened: main point is emphasized. Testimony is leveled: context, details, qualifications are de-emphasized or ignored.• Familiar Examples:• Experience w/ newspaper stories of events you’ve attended.• Individuals who you’ve heard stories about. Often disappointed to meet them.Secondhand accounts become simpler/ “cleaner” stories.Expected ValueThe expected value tells you what your average (mean) value is per decision given in terms of money.So the basic idea is that (if you only care about money), the thing to do is to take the option that maximizesexpected value.Maximizing the expected value is not always the rational decision. - Be able to do the calculation: EV (Option) = [Pr(Outcome a) x Value(Outcome a)] + [Pr(Outcome b x Value(Outcome b)] +… So essentially it is for every option you multiply each outcome with each value and then add all of them together to get the expected value for that option. - Expected Utility—the expected utility tells you what your average (mean) value is per decision given in terms how “good” we think that outcome would be. “Good” in expected utility can mean lots of things such as happiness, satisfaction of preferences, pleasure. Whenever we’re faced with a decision, we can